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AWS Machine Learning Specialty Certification Course (EARLY ACCESS)
INTRODUCTION TO THE COURSE
EARLY ACCESS Course Introduction (3:14)
About Machine Learning (13:57)
About the AWS Certification - AWS MLS-C01 (16:53)
About This Course (7:20)
Resources & Support (4:11)
INTRODUCTION TO MACHINE LEARNING
My First Model (13:25)
Problem Space (7:26)
Machine Learning Life Cycle (6:27)
Types of Machine Learning (11:03)
Build: Machine Learning Environment - Part 1 (9:10)
Build: Machine Learning Environment - Part 2 (11:09)
Build: My First Model - Part 1 (12:40)
Build: My First Model - Part 2 (11:27)
Build: My First Model - Part 3 (10:05)
DATA
Where to Find Data (10:03)
Build: Loading Sample Data with scikit-learn “California Housing” (25:44)
Build: Loading Sample Data with scikit-learn “MNIST hand-written digits” (11:43)
Build: Create Sample Data with scikit-learn “Random regression problem.” (12:03)
Build: Loading Data from S3 into a Notebook (10:21)
Data Exploration: Useless Data (5:04)
Data Exploration: Binary & Continuous (4:18)
Data Exploration: Categorical (6:34)
Data Exploration: Text & Temporal (3:35)
Feature Encoding (9:42)
Build: Feature Encoding in Jupyter (14:37)
Text Encoding (7:44)
Missing Data (8:28)
Build: Imputation with Jupyter (14:44)
Unbalanced Data (7:50)
Feature Engineering (6:59)
ALGORITHMS
Updating the Machine Learning Environment (4:03)
Logistic Regression (10:35)
Build: Logistic Regression with scikit-learn (13:51)
Linear Regression & Stochastic Gradient Descent (12:44)
Build: Linear Regression (13:54)
Build: Linear Regression & SGD (5:39)
Support Vector Machines (10:54)
Build: Support Vector Machines 1 (13:06)
Build: Support Vector Machines 2 (12:55)
Decision Trees Part 1 (12:22)
Decision Trees Part 2 (10:57)
Random Forests (7:19)
Build: Random Forests (19:38)
K-means Clustering (13:51)
Build: K-means Clustering (12:17)
K-Nearest Neighbours (3:17)
Build: K-nearest Neighbors (7:07)
Latent Dirichlet Allocation (LDA) (15:51)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 1 (9:45)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 2 (10:29)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 3 (6:25)
Mini Project: Latent Dirichlet Allocation (LDA) - Part 4 (11:03)
Principal Component Analysis (PCA) (12:11)
Build: Principal Component Analysis (PCA) (9:48)
Introduction to Neural Networks (12:46)
Inside the Neuron (16:53)
Training a Neural Network (19:47)
Build: My First Neural Network - Part 1 (9:29)
Build: My First Neural Network - Part 2 (13:33)
Build: MNIST Handwritten Dataset (12:27)
Matchbox Tic-Tac-Toe (11:22)
CNN (Convolutional Neural Networks) Part 1 (16:28)
CNN (Convolutional Neural Networks) Part 2 (10:44)
Build: CNN Lego Sorting - Part 1 (14:27)
Build: CNN Lego Sorting - Part 2 (18:27)
RNN (Recurrent Neural Networks) (12:08)
Build: RNN (Recurrent Neural Networks) (15:10)
Word2vec (15:30)
Demo: Word2vec with the (tiny) h2o dataset (6:33)
Build: Word2vec - Part 1 (13:36)
Build: Word2vec: Part 2 (12:30)
Seq2seq (6:57)
TRAINING
Prepare Data for Training (18:05)
K-Fold Cross Validation (6:12)
Bias and Variance (6:27)
Regularization, L1 & L2 (10:46)
Hyperparameters! (15:31)
Optimizers (11:43)
Transfer Learning (7:32)
Training Compute Architecture (7:58)
TESTING & PERFORMANCE
Confusion Matrix (Coming soon...)
Accuracy (Coming soon...)
Sensitivity & Specificity (Coming soon...)
F1 Score (Coming soon...)
ROC AUC (Coming soon...)
Performance Issues and Solutions (Coming soon...)
HOSTING & INFERENCE
Inference (Coming soon...)
Build: (Coming soon...)
TOOLS & FRAMEWORKS
Frameworks (Coming soon...)
Build: Example datasets from frameworks (Coming soon...)
AWS AI SERVICES (Coming soon...)
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AMAZON SAGEMAKER (Coming soon...)
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OTHER AWS SERVICES (Coming soon...)
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ROC AUC (Coming soon...)
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